Metadata-Version: 2.1
Name: scanpy-scripts
Version: 0.2.13
Summary: Scripts for using scanpy from the command line
Home-page: https://github.com/ebi-gene-expression-group/scanpy-scripts
Author: nh3
Author-email: nh3@users.noreply.github.com
License: UNKNOWN
Description: # scanpy-scripts
        Scripts for using scanpy from the command line
        
        In order to wrap scanpy's internal workflow in any given workflow language, it's important to have scripts to call each of those steps. These scripts are being written here, and will improve in completeness as time progresses. 
        
        ## Install
        
        ```bash
        conda install scanpy-scripts
        # or
        pip3 install scanpy-scripts
        ```
        
        ## Test installation
        
        There is an example script included:
        
        ```bash
        scanpy-scripts-tests.sh
        ```
        
        This downloads [a well-known test 10X dataset]('https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz) and executes all of the scripts described below.
        
        ## Commands
        
        Available commands are described below. Each has usage instructions available via --help, consult function documentation in scanpy for further details.
        
        ```
        Usage: scanpy-cli [OPTIONS] COMMAND [ARGS]...
        
          Command line interface to [scanpy](https://github.com/theislab/scanpy)
        
        Options:
          --debug              Print debug information
          --verbosity INTEGER  Set scanpy verbosity
          --version            Show the version and exit.
          --help               Show this message and exit.
        
        Commands:
          read       Read 10x data and save in specified format.
          filter     Filter data based on specified conditions.
          norm       Normalise data per cell.
          hvg        Find highly variable genes.
          scale      Scale data per gene.
          regress    Regress-out observation variables.
          pca        Dimensionality reduction by PCA.
          neighbor   Compute a neighbourhood graph of observations.
          embed      Embed cells into two-dimensional space.
          cluster    Cluster cells into sub-populations.
          diffexp    Find markers for each clusters.
          paga       Trajectory inference by abstract graph analysis.
          dpt        Calculate diffusion pseudotime relative to the root cells.
          integrate  Integrate cells from different experimental batches.
          plot       Visualise data.
          ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
